MRI is an imaging method which excites nuclear spin of an object set in a static magnetic field with a RF (radio frequency) signal having the Larmor frequency magnetically and reconstruct an image based on NMR (nuclear magnetic resonance) signals generated due to the excitation.
AFI is known as an imaging method for MRI. In the AFI method, asymmetric data is sampled in the wave number direction in k-space and a phase distribution estimated based on the sampled self data is used to phase correction, and subsequently image data is reconstructed. By the AFI method, image data equivalent to one generated from data symmetrically sampled in k-space can be generated.
For the AFI method, various methods such as Margosian method, FIR (finite impulse response) method, MoFIR (Modified FIR) method, POCS (projection on to convex sets) method, hybrid method and the like are suggested. Further, another asymmetric data sampling method, by which data is asymmetrically sampled in k-space and FT (Fourier transform) is performed after 0-filling on parts having no data, is known though the method is not an AFI method.
In the Margosian method, a homodyne filter as a window function is applied to asymmetrically sampled k-space data, and subsequently, r-space data corresponding to the asymmetric k-space data is generated by FT. On the other hand, a phase distribution is estimated based on symmetrically sampled k-space data in a low frequency region near the center in k-space out of the asymmetrically sampled k-space data. Then, a phase correction using the estimated phase distribution is performed on the r-space data corresponding to the asymmetric k-space data.
The POCS method is an improved Margosian method in which POCS loop processing is performed after the Margosian method. The POCS loop processing is a processing to converge a change in imaginary part not to exceed a threshold by repeating realization processing, compound processing and phase correction processing. The realization processing is processing for making the imaginary part of the r-space data after the phase correction zero to remain the real part. The compound processing is processing for combining a non-sampling part of k-space data, which is obtained by returning a phase of the realized r-space data and subsequently IFT (inverse Fourier transform), with a sampling part of the original data. The phase correction processing is applied to r-space data obtained by FT of the k-space data after the compound processing. The POCS method is based on the principle that the imaginary part of the r-space data becomes zero so long as the phase correction is complete. The POCS method can reduced errors in the phase correction occurring due to the homodyne filter processing in the Margosian method by repeating the POCS loop processing a few times.
On the other hand, in the FIR method, the phase correction is performed before applying the homodyne filter to the asymmetrically sampled k-space data. Specifically, in the FIR method, the phase correction is performed to the r-space data generated by FT of the asymmetric k-space data, and subsequently, the r-space data after the phase correction is transformed to k-space data by IFT. Then, the homodyne filter is applied to the k-space data after the phase correction. In this FIR method, the phase correction is performed before the homodyne filter processing though the data processing period becomes longer than that in the Margosian method by two times FT. Therefore, errors in the phase correction due to the homodyne filter processing can be reduced.
The MoFIR method is an improved FIR method in which the phase distribution for the phase correction is estimated based on whole k-space data including the part where the k-space data is asymmetrically sampled as well as the low frequency region where the k-space data is symmetrically sampled. Specifically, the MoFIR method estimates the phase distribution for the phase correction based on the whole k-space data asymmetrically sampled while the FIR method estimates the phase distribution in a low frequency region near the center of k-space for the phase correction from the k-space data only in the low frequency region. Therefore, the MoFIR method makes it possible to estimate a phase distribution in a higher frequency region compared to the FIR method though the estimated phase distribution differs from the actual phase distribution. Consequently, the MoFIR method yields reduction of phase correction errors due to the homodyne filter processing in the Margosian method and the FIR method.
On the other hand, 0-filling, which is the simplest reconstruction method based on asymmetrically sampled data, generates blurring in images. However, in case of asymmetrically sampling at a relatively low asymmetric degree, such as a case data more than 70% is symmetrically sampled, blurring in images becomes acceptable. In addition, 0-filling does not require special processing and does not generate artifacts due to excessive phase correction in the AFI. Therefore, 0-filling is still used abundantly when an asymmetric degree is relatively low.
The hybrid method is a method derived by combining 0-filling with the AFI method. That is, the hybrid method is a technique in which a 0-fill image generated by 0-filling is combined with an AFI image generated by the AFI by a weighted addition. More specifically, the weight is adjusted so that parts each having a larger difference in phase or amplitude between the 0-fill image and the AFI image become the 0-fill image more while parts each having a smaller difference become the AFI image more.
The conventional AFI has a problem that artifacts sometimes occur more remarkably than 0-filling when a phase error becomes large due to failure in estimation of the phase. Accordingly, it is desired to generate image data from data asymmetrically sampled in k-space with accuracy equivalent to that of an image generated from symmetrically sampled data while increase of a data processing period necessary for image reconstruction is minimized.
The object of the present invention is to provide a magnetic resonance imaging apparatus and a magnetic resonance imaging method which can generate image data having higher accuracy based on MR data asymmetrically sampled in k-space with suppressing increase of a data processing period.